






"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__lowerCamelCase							=  logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCamelCase							=  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    List[Any]=8  )    ->							Any:
						__magic_name__:	List[str]							=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__magic_name__:	Any							=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



def   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Any=5_1_2      ,   __UpperCAmelCase      :    str=5_1_2  )    ->							int:
						__magic_name__:	int							=       pil_image.resize((w, h)      ,   resample=Image.BICUBIC      ,   reducing_gap=1  )
						__magic_name__:	str							=       np.array(pil_image.convert("""RGB"""  )  )
						__magic_name__:	str							=       arr.astype(np.floataa  ) / 1_27.5 - 1
						__magic_name__:	Dict							=       np.transpose(__UpperCAmelCase      ,   [2, 0, 1]  )
						__magic_name__:	Union[str, Any]							=       torch.from_numpy(__UpperCAmelCase  ).unsqueeze(0  )
						return image



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      UNetaDConditionModel     ,		__snake_case  :      DDPMScheduler     ,		__snake_case  :      VQModel     ,		)			->							List[str]:
											super().__init__()

											self.register_modules(
											    unet=__snake_case     ,		scheduler=__snake_case     ,		movq=__snake_case     ,		)
											__magic_name__:	Tuple							=       2 ** (len(self.movq.config.block_out_channels					) - 1)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Any					)			->							Optional[Any]:
											# get the original timestep using init_timestep
											__magic_name__:	str							=       min(int(num_inference_steps * strength					)     ,		__snake_case					)

											__magic_name__:	Dict							=       max(num_inference_steps - init_timestep     ,		0					)
											__magic_name__:	Tuple							=       self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=None					)			->							Optional[int]:
											if not isinstance(__snake_case     ,		(torch.Tensor, PIL.Image.Image, list)					):
																	raise ValueError(
																	    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case					)}'					)

											__magic_name__:	Union[str, Any]							=       image.to(device=__snake_case     ,		dtype=__snake_case					)

											__magic_name__:	List[str]							=       batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	__magic_name__:	Dict							=       image

											else:
																	if isinstance(__snake_case     ,		__snake_case					) and len(__snake_case					) != batch_size:
																							raise ValueError(
																							    F'You have passed a list of generators of length {len(__snake_case					)}, but requested an effective batch'
																							    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

																	elif isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	str							=       [
																							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__snake_case					)
																							]
																							__magic_name__:	List[Any]							=       torch.cat(__snake_case     ,		dim=0					)
																	else:
																							__magic_name__:	Any							=       self.movq.encode(__snake_case					).latent_dist.sample(__snake_case					)

																	__magic_name__:	str							=       self.movq.config.scaling_factor * init_latents

											__magic_name__:	Optional[Any]							=       torch.cat([init_latents]     ,		dim=0					)

											__magic_name__:	Optional[Any]							=       init_latents.shape
											__magic_name__:	Dict							=       randn_tensor(__snake_case     ,		generator=__snake_case     ,		device=__snake_case     ,		dtype=__snake_case					)

											# get latents
											__magic_name__:	Optional[Any]							=       self.scheduler.add_noise(__snake_case     ,		__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[Any]							=       init_latents

											return latents

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Tuple=0					)			->							Tuple:
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

											__magic_name__:	str							=       torch.device(F'cuda:{gpu_id}'					)

											__magic_name__:	List[str]							=       [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Optional[Any]=0					)			->							Any:
											if is_accelerate_available() and is_accelerate_version(""">="""     ,		"""0.17.0.dev0"""					):
																	from accelerate import cpu_offload_with_hook
											else:
																	raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

											__magic_name__:	Any							=       torch.device(F'cuda:{gpu_id}'					)

											if self.device.type != "cpu":
																	self.to("""cpu"""     ,		silence_dtype_warnings=__snake_case					)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__magic_name__:	Tuple							=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	__magic_name__,			__magic_name__:	Any							=       cpu_offload_with_hook(__snake_case     ,		__snake_case     ,		prev_module_hook=__snake_case					)

											# We'll offload the last model manually.
											__magic_name__:	List[Any]							=       hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											if not hasattr(self.unet     ,		"""_hf_hook"""					):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__snake_case     ,		"""_hf_hook"""					)
																	    and hasattr(module._hf_hook     ,		"""execution_device"""					)
																	    and module._hf_hook.execution_device is not None
																	):
																							return torch.device(module._hf_hook.execution_device					)
											return self.device






					@torch.no_grad()
					@replace_example_docstring(__snake_case					)
					def __call__(			self  :      List[Any]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 1_0_0     ,		__snake_case  :      float = 4.0     ,		__snake_case  :      float = 0.3     ,		__snake_case  :      int = 1     ,		__snake_case  :      Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__snake_case  :      Optional[str] = "pil"     ,		__snake_case  :      bool = True     ,		)			->							List[Any]:
											__magic_name__:	Tuple							=       self._execution_device

											__magic_name__:	str							=       guidance_scale > 1.0

											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Optional[int]							=       torch.cat(__snake_case     ,		dim=0					)
											__magic_name__:	Any							=       image_embeds.shape[0]
											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	str							=       torch.cat(__snake_case     ,		dim=0					)

											if do_classifier_free_guidance:
																	__magic_name__:	List[str]							=       image_embeds.repeat_interleave(__snake_case     ,		dim=0					)
																	__magic_name__:	Optional[int]							=       negative_image_embeds.repeat_interleave(__snake_case     ,		dim=0					)

											__magic_name__:	str							=       torch.cat([negative_image_embeds, image_embeds]     ,		dim=0					).to(dtype=self.unet.dtype     ,		device=__snake_case					)

											if not isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Dict							=       [image]
											if not all(isinstance(__snake_case     ,		(PIL.Image.Image, torch.Tensor)					) for i in image					):
																	raise ValueError(
																	    F'Input is in incorrect format: {[type(__snake_case					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'					)

											__magic_name__:	List[Any]							=       torch.cat([prepare_image(__snake_case     ,		__snake_case     ,		__snake_case					) for i in image]     ,		dim=0					)
											__magic_name__:	Optional[Any]							=       image.to(dtype=image_embeds.dtype     ,		device=__snake_case					)

											__magic_name__:	Tuple							=       self.movq.encode(__snake_case					)["""latents"""]
											__magic_name__:	str							=       latents.repeat_interleave(__snake_case     ,		dim=0					)
											self.scheduler.set_timesteps(__snake_case     ,		device=__snake_case					)
											__magic_name__,			__magic_name__:	Tuple							=       self.get_timesteps(__snake_case     ,		__snake_case     ,		__snake_case					)
											__magic_name__:	Tuple							=       timesteps[:1].repeat(batch_size * num_images_per_prompt					)
											__magic_name__,			__magic_name__:	str							=       downscale_height_and_width(__snake_case     ,		__snake_case     ,		self.movq_scale_factor					)
											__magic_name__:	List[Any]							=       self.prepare_latents(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		image_embeds.dtype     ,		__snake_case     ,		__snake_case					)
											for i, t in enumerate(self.progress_bar(__snake_case					)					):
																	# expand the latents if we are doing classifier free guidance
																	__magic_name__:	Union[str, Any]							=       torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																	__magic_name__:	Optional[Any]							=       {"""image_embeds""": image_embeds}
																	__magic_name__:	str							=       self.unet(
																	    sample=__snake_case     ,		timestep=__snake_case     ,		encoder_hidden_states=__snake_case     ,		added_cond_kwargs=__snake_case     ,		return_dict=__snake_case     ,		)[0]

																	if do_classifier_free_guidance:
																							__magic_name__,			__magic_name__:	List[Any]							=       noise_pred.split(latents.shape[1]     ,		dim=1					)
																							__magic_name__,			__magic_name__:	Optional[int]							=       noise_pred.chunk(2					)
																							__magic_name__,			__magic_name__:	str							=       variance_pred.chunk(2					)
																							__magic_name__:	int							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							__magic_name__:	Optional[Any]							=       torch.cat([noise_pred, variance_pred_text]     ,		dim=1					)

																	if not (
																	    hasattr(self.scheduler.config     ,		"""variance_type"""					)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																							__magic_name__,			__magic_name__:	str							=       noise_pred.split(latents.shape[1]     ,		dim=1					)

																	# compute the previous noisy sample x_t -> x_t-1
																	__magic_name__:	List[str]							=       self.scheduler.step(
																	    __snake_case     ,		__snake_case     ,		__snake_case     ,		generator=__snake_case     ,		)[0]

											# post-processing
											__magic_name__:	Any							=       self.movq.decode(__snake_case     ,		force_not_quantize=__snake_case					)["""sample"""]

											if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'					)

											if output_type in ["np", "pil"]:
																	__magic_name__:	Any							=       image * 0.5 + 0.5
																	__magic_name__:	int							=       image.clamp(0     ,		1					)
																	__magic_name__:	Union[str, Any]							=       image.cpu().permute(0     ,		2     ,		3     ,		1					).float().numpy()

											if output_type == "pil":
																	__magic_name__:	Dict							=       self.numpy_to_pil(__snake_case					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__snake_case					)






import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)



class        A__(unittest.TestCase							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=4  ,      )     -> Tuple:
									a_						:						Tuple 							= parent
									a_						:						Optional[Any] 							= batch_size
									a_						:						int 							= seq_length
									a_						:						List[str] 							= is_training
									a_						:						str 							= use_attention_mask
									a_						:						Optional[int] 							= use_token_type_ids
									a_						:						Optional[int] 							= use_labels
									a_						:						int 							= vocab_size
									a_						:						Union[str, Any] 							= hidden_size
									a_						:						Optional[int] 							= num_hidden_layers
									a_						:						Optional[int] 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						int 							= hidden_act
									a_						:						Any 							= hidden_dropout_prob
									a_						:						int 							= attention_probs_dropout_prob
									a_						:						List[str] 							= max_position_embeddings
									a_						:						Optional[int] 							= type_vocab_size
									a_						:						List[str] 							= type_sequence_label_size
									a_						:						Optional[Any] 							= initializer_range
									a_						:						Optional[int] 							= num_choices





						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						int 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						Dict 							= None
									if self.use_attention_mask:
												a_						:						List[str] 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						List[str] 							= None
									if self.use_token_type_ids:
												a_						:						Dict 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Any 							= RobertaPreLayerNormConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )

									return config, input_ids, token_type_ids, attention_mask





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						Any 							= config_and_inputs
									a_						:						List[Any] 							= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
									return config, inputs_dict





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[Any] 							= self.prepare_config_and_inputs()
									a_							,					a_							,					a_							,					a_						:						str 							= config_and_inputs

									a_						:						str 							= True
									a_						:						Any 							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      vocab_size=2    )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				List[str]       					=			True

						_A    :				int       					=			(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= FlaxRobertaPreLayerNormModelTester(self    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									for model_class_name in self.all_model_classes:
												a_						:						Union[str, Any] 							= model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )
												a_						:						Union[str, Any] 							= model(np.ones((1, 1)    )    )
												self.assertIsNotNone(_lowercase    )







@require_flax
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@slow
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						Union[str, Any] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						Tuple 							= model(_lowercase    )[0]
									a_						:						Tuple 							= [1, 11, 50_265]
									self.assertEqual(list(output.shape    )  ,      _lowercase    )
									# compare the actual values for a slice.
									a_						:						Tuple 							= np.array(
									    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )





						@slow
						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						Union[str, Any] 							= FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,      from_pt=_lowercase    )

									a_						:						List[str] 							= np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  ,      dtype=jnp.intaa    )
									a_						:						str 							= model(_lowercase    )[0]
									# compare the actual values for a slice.
									a_						:						Union[str, Any] 							= np.array(
									    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]  ,      dtype=np.floataa    )
									self.assertTrue(np.allclose(output[:, :3, :3]  ,      _lowercase  ,      atol=1e-4    )    )


